1. The EXPLAIN QUERY PLAN Command

Warning: The data returned by the EXPLAIN QUERY PLAN command is
intended for interactive debugging only. The output format may change
between SQLite releases. Applications should not depend on the output
format of the EXPLAIN QUERY PLAN command.

Alert: As warned above, the EXPLAIN QUERY PLAN output format did
change substantially with the version 3.24.0 release (2018-06-04).
Further changes are possible in subsequent releases.

The EXPLAIN QUERY PLAN SQL command is used to obtain a high-level
description of the strategy or plan that SQLite uses to implement a specific
SQL query. Most significantly, EXPLAIN QUERY PLAN reports on the way in
which the query uses database indices. This document is a guide to
understanding and interpreting the EXPLAIN QUERY PLAN output. Background
information is available separately:

A query plan is represented as a tree.
In raw form, as returned by sqlite3_step(), each node of the tree
consists of four fields: An integer node id, an integer parent id,
an auxiliary integer field that is not currently used, and a description
of the node.
The entire tree is therefore a table with four columns and zero or more
rows.
The command-line shell will usually intercept this table and renders
it as an ASCII-art graph for more convenient viewing. To defeat the
shells automatic graph rendering, simply include extra white space
in between any of the "EXPLAIN", "QUERY", and/or "PLAN" keywords and
the output will appear in a (less helpful) tabular format.

One can also set the CLI into automatic EXPLAIN QUERY PLAN mode
using the ".eqp on" command:

sqlite> .eqp on

In automatic EXPLAIN QUERY PLAN mode, the shell automatically runs
a separate EXPLAIN QUERY PLAN query for each statement you enter and
displays the result before actually running the query. Use the
".eqp off" command to turn automatic EXPLAIN QUERY PLAN mode back off.

EXPLAIN QUERY PLAN is most useful on a SELECT statement,
but may also appear with other statements that read data from database
tables (e.g. UPDATE, DELETE, INSERT INTO ... SELECT).

1.1. Table and Index Scans

When processing a SELECT (or other) statement, SQLite may retrieve data from
database tables in a variety of ways. It may scan through all the records in
a table (a full-table scan), scan a contiguous subset of the records in a
table based on the rowid index, scan a contiguous subset of the entries in a
database index, or use a combination of the above strategies
in a single scan. The various ways in which SQLite may retrieve data from a
table or index are described in detail here.

For each table read by the query, the output of EXPLAIN QUERY
PLAN includes a record for which the value in the "detail" column begins
with either "SCAN" or "SEARCH". "SCAN" is used for a full-table scan,
including cases where SQLite iterates through all records in a table
in an order defined by an index. "SEARCH" indicates that only a subset of
the table rows are visited. Each SCAN or SEARCH record includes the
following information:

The example above shows
SQLite picking full-table scan will visit all rows in the table.
If the query were able to use an index, then the
SCAN/SEARCH record would include the name of the index and, for a
SEARCH record, an indication of how the subset of rows visited is
identified. For example:

The previous example, SQLite uses index "i1" to optimize
a WHERE clause term of the form (a=?) - in this case "a=1".
The previous example could not use a covering index, but the following
example can, and that fact is reflected in the output:

All joins in SQLite are implemented using nested scans. When a
SELECT query that features a join is analyzed using EXPLAIN QUERY PLAN, one
SCAN or SEARCH record is output for each nested loop. For example:

The order of the entries indicates the nesting order. In
this case, the scan of table t1 using index i2 is the outer loop (since it
appears first)
and the full-table scan of table t2 is the inner loop (since it appears
last).
In the following example, the positions of t1 and t2 in the FROM
clause of the SELECT are reversed. The query strategy remains the same.
The output from EXPLAIN QUERY PLAN shows how the query is actually
evaluated, not how it is specified in the SQL statement.

If the WHERE clause of a query contains an OR expression, then SQLite might
use the "OR by union" strategy (also known as the
OR optimization). In this case there will be single top-level record
for the search, with two sub-records, one for each index:

1.2. Temporary Sorting B-Trees

If a SELECT query contains an ORDER BY, GROUP BY or DISTINCT clause,
SQLite may need to use a temporary b-tree structure to sort the output
rows. Or, it might use an index. Using an index is
almost always much more efficient than performing a sort.
If a temporary b-tree is required, a record is added to the EXPLAIN
QUERY PLAN output with the "detail" field set to a string value of
the form "USE TEMP B-TREE FOR xxx", where xxx is one of "ORDER BY",
"GROUP BY" or "DISTINCT". For example:

The example above contains two "SCALAR" subqueries. The subqueries
are SCALAR in the sense that they return a single value - a one-row,
one-column table. If the actual query returns more than that, then
only the first column of the first row is used.

The first subquery above is constant with respect to the outer query.
The value for the first subquery can be computed once and then reused
for each row of the outer SELECT. The second subquery, however, is
"CORRELATED". The value of the second subquery changes depending
on values in the current row of the outer query. Hence, the second
subquery must be run once for each output row in the outer SELECT.

Unless the flattening optimization is applied, if a subquery appears in
the FROM clause of a SELECT statement, SQLite can either run the subquery and
stores the results in a temporary table, or it can run the subquery as a
co-routine. The following query is an example of the latter. The subquery
is run by a co-routine. The outer query blocks whenever it needs another
row of input from the subquery. Control switches to the co-routine which
produces the desired output row, then control switches back to the main
routine which continues processing.

If the flattening optimization is used on a subquery in the FROM clause
of a SELECT statement, that effectively merges the subquery into the outer
query. The output of EXPLAIN QUERY PLAN reflects this, as in the following
example:

If the content of a subquery might need to be visited more than once, then
the use of a co-routine is undesirable, as the co-routine would then have to
compute the data more than once. And if the subquery cannot be flattened,
that means the subquery must be manifested into a transient table.

The "USING TEMP B-TREE" clause in the above output indicates that a
temporary b-tree structure is used to implement the UNION of the results
of the two sub-selects. An alternative method of computing a compound
is to run each subquery as a co-routine, arrange for their outputs to
appear in sorted order, and merge the results together. When the query
planner chooses this latter approach, the EXPLAIN QUERY PLAN output
looks like this: